Detection of common risk factors for diagnosis of cardiac arrhythmia using machine learning algorithm

心律失常 计算机科学 人工智能 机器学习 算法 内科学 医学 心房颤动
作者
Samir S. Yadav,Shivajirao M. Jadhav
出处
期刊:Expert Systems With Applications [Elsevier BV]
卷期号:163: 113807-113807 被引量:42
标识
DOI:10.1016/j.eswa.2020.113807
摘要

This article aims to establish an accurate and innovative objective framework for classification of cardiac arrhythmia patients by trying to measure the importance of specific factors that are potentially relevant to its diagnosis. Cardiac arrhythmia (CA) is a group of condition related to the irregular heartbeats. It is very essential to prevent a CAs, as they are the most common cause of natural death in all over the world. According to the health reports, more than 4.5 lakh cardiac patients fatalities annually in the United States alone. To diagnose cardiac diseases, patient’s reported qualitative symptoms can be useful. However, this strategy may fail sometimes due to less accuracy and false positive cases. Therefore in this work, we strive to find a quantitative basis for more reliable and accurate diagnosis of cardiac arrhythmias. This research used the openly available MIMIC-III database to obtain large quantities of clinical monitoring data from patients over the age of sixteen admitted to intensive care units (ICUs). The database was processed on the Health Sciences and Technology (HEST) Cluster, filtered with in a specified time frame(24hrs, 12hrs and 6hrs) and organized into a multi-class and a single-class and finally split into train, validation, and test sets with respective weights of 0.7, 0.2, and 0.1. We used random forest classifier model for the diagnosis of cardiac arrhythmia and measure the importance of different features like respiratory rate, blood pressure, sodium, potassium, calcium, among the other features. Hyperparameter optimization techniques like grid search and genetic algorithms are compared to find the maximum number and depth of trees in the forest. The model achieved, at its best, an Area Under the Receiver Operator Curve (AUC) score of 0.9787 and, thus, confirmed the importance of several previously suggested factors in the diagnosis of cardiac arrhythmias. We substantiated claims that each of sodium, calcium, potassium, respiratory rates and blood pressure can be used for the early diagnosis of cardiac arrhythmias.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刘畅发布了新的文献求助10
刚刚
852应助迷人的学术妲己采纳,获得10
刚刚
lichee完成签到,获得积分10
刚刚
夏晴晴发布了新的文献求助30
1秒前
wbgwudi完成签到,获得积分10
1秒前
顾矜应助SHUIw采纳,获得10
1秒前
天天快乐应助蔫蔫采纳,获得10
2秒前
ddsyg126发布了新的文献求助10
2秒前
XinZhang关注了科研通微信公众号
2秒前
2秒前
NexusExplorer应助一漾采纳,获得10
3秒前
Archer发布了新的文献求助30
4秒前
青山完成签到,获得积分10
4秒前
不想发布了新的文献求助10
4秒前
枫叶完成签到 ,获得积分10
5秒前
脑洞疼应助DYQin采纳,获得10
5秒前
5秒前
北有云烟完成签到 ,获得积分10
5秒前
丘比特应助文静采纳,获得10
6秒前
专注的问筠完成签到,获得积分10
7秒前
7秒前
orixero应助Zeus采纳,获得50
7秒前
111111111发布了新的文献求助10
7秒前
共享精神应助PengHu采纳,获得10
7秒前
哈牛发布了新的文献求助10
7秒前
river_121完成签到,获得积分10
8秒前
8秒前
徐大头发布了新的文献求助10
8秒前
代纤绮完成签到,获得积分10
8秒前
鲜艳的访风完成签到,获得积分10
8秒前
9秒前
9秒前
qianhua发布了新的文献求助10
10秒前
jichups完成签到,获得积分10
10秒前
小蘑菇应助jmy1995采纳,获得10
10秒前
刘欣完成签到,获得积分10
11秒前
zyx完成签到 ,获得积分10
11秒前
lyh发布了新的文献求助10
11秒前
不闻不问完成签到,获得积分10
13秒前
13秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3969060
求助须知:如何正确求助?哪些是违规求助? 3513962
关于积分的说明 11171223
捐赠科研通 3249302
什么是DOI,文献DOI怎么找? 1794772
邀请新用户注册赠送积分活动 875377
科研通“疑难数据库(出版商)”最低求助积分说明 804769